{"title":"Instrumental variable quantile regression for clustered data","authors":"G. Besstremyannaya, S. Golovan","doi":"10.1016/j.ecosta.2023.06.005","DOIUrl":"https://doi.org/10.1016/j.ecosta.2023.06.005","url":null,"abstract":"","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"4 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75554052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Estimating a discrete distribution subject to random left-truncation with an application to structured finance","authors":"Jackson P. Lautier, V. Pozdnyakov, Jun Yan","doi":"10.1016/j.ecosta.2023.05.005","DOIUrl":"https://doi.org/10.1016/j.ecosta.2023.05.005","url":null,"abstract":"","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"27 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84208774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Highly Efficient Estimators with High Breakdown Point for Linear Models with Structured Covariance Matrices","authors":"H. P. Lopuhaä","doi":"10.1016/j.ecosta.2023.03.003","DOIUrl":"https://doi.org/10.1016/j.ecosta.2023.03.003","url":null,"abstract":"","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"194 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73044459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"High-Dimensional Dynamic Factor Models: A Selective Survey and Lines of Future Research","authors":"Marco Lippi , Manfred Deistler , Brian Anderson","doi":"10.1016/j.ecosta.2022.03.008","DOIUrl":"https://doi.org/10.1016/j.ecosta.2022.03.008","url":null,"abstract":"<div><p>High-Dimensional Dynamic Factor Models are presented in detail: The main assumptions and their motivation, main results, illustrations by means of elementary examples. In particular, the role of singular ARMA models in the theory and applications of High-Dimensional Dynamic Factor Models is discussed. The emphasis is on model classes and their structure theory, rather than on estimation in the narrow sense. The survey is not comprehensive. Its aim is to point out promising lines of research and applications that have not yet been sufficiently developed.</p></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"26 ","pages":"Pages 3-16"},"PeriodicalIF":1.9,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50193193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Semi-Supervised Learning of Classifiers from a Statistical Perspective: A Brief Review","authors":"Daniel Ahfock, Geoffrey J. McLachlan","doi":"10.1016/j.ecosta.2022.03.007","DOIUrl":"https://doi.org/10.1016/j.ecosta.2022.03.007","url":null,"abstract":"<div><p>There has been increasing attention to semi-supervised learning (SSL) approaches in machine learning to forming a classifier in situations where the training data for a classifier consists of a limited number of classified observations but a much larger number of unclassified observations. This is because the procurement of classified data can be quite costly due to high acquisition costs and subsequent financial, time, and ethical issues that can arise in attempts to provide the true class labels for the unclassified data that have been acquired. A review is provided of statistical SSL approaches to this problem, focussing on the recent result that a classifier formed from a partially classified sample can actually have smaller expected error rate than that if the sample were completely classified. This rather paradoxical outcome is able to be achieved by introducing a framework with a missingness mechanism for the missing labels of the unclassified observations. It is most relevant in commonly occurring situations in practice, where the unclassified data occur primarily in regions of relatively high entropy in the feature space thereby making it difficult for their class labels to be easily obtained.</p></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"26 ","pages":"Pages 124-138"},"PeriodicalIF":1.9,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50193293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jennifer L. Castle , Jurgen A. Doornik , David F. Hendry
{"title":"Robust Discovery of Regression Models","authors":"Jennifer L. Castle , Jurgen A. Doornik , David F. Hendry","doi":"10.1016/j.ecosta.2021.05.004","DOIUrl":"https://doi.org/10.1016/j.ecosta.2021.05.004","url":null,"abstract":"<div><p>Successful modeling of observational data requires jointly discovering the determinants of the underlying process and the observations from which it can be reliably estimated, given the near impossibility of pre-specifying both. To do so requires avoiding many potential problems, including substantive omitted variables; unmodeled non-stationarity and misspecified dynamics in time series; non-linearity; and inappropriate conditioning assumptions, as well as incorrect distributional shape combined with contaminated observations from outliers and shifts. The aim is to discover robust, parsimonious representations that retain the relevant information, are well specified, encompass alternative models, and evaluate the validity of the study. An approach is proposed that provides robustness in many directions. It is demonstrated how to handle apparent outliers due to alternative distributional assumptions; and discriminate between outliers and large observations arising from non-linear responses. Two empirical applications, utilizing datasets popularized in previous applications, show substantive improvements from the proposed approach to robust model discovery.</p></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"26 ","pages":"Pages 31-51"},"PeriodicalIF":1.9,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.ecosta.2021.05.004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50193247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Addressing robust estimation in covariate–specific ROC curves","authors":"Ana M. Bianco, G. Boente","doi":"10.1016/j.ecosta.2023.04.001","DOIUrl":"https://doi.org/10.1016/j.ecosta.2023.04.001","url":null,"abstract":"","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"15 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80124976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Fuzzy sets and (fuzzy) random sets in Econometrics and Statistics","authors":"Ana Colubi , Ana Belén Ramos-Guajardo","doi":"10.1016/j.ecosta.2022.07.001","DOIUrl":"https://doi.org/10.1016/j.ecosta.2022.07.001","url":null,"abstract":"<div><p>Fuzzy sets generalize the concept of sets by considering that elements belong to a class (or fulfil a property) with a degree of membership (or certainty) ranging between 0 and 1. Fuzzy sets have been used in diverse areas to model gradual transitions as opposite to abrupt changes. In econometrics<span> and statistics<span><span>, this has been especially relevant in clustering, regression discontinuity designs, and imprecise data modelling, to name but a few. Although the membership functions vary between 0 and 1 as the </span>probabilities, the nature of the imprecision captured by the fuzzy sets is usually different from stochastic uncertainty. The aim is to illustrate the advantages of combining fuzziness, imprecision, or partial knowledge with randomness through various key methodological problems. Emphasis will be placed on the management of non-precise data modelled through (fuzzy) sets. Software to apply the reviewed methodology will be suggested. Some open problems that could be of future interest will be discussed.</span></span></p></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"26 ","pages":"Pages 84-98"},"PeriodicalIF":1.9,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50193291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"When the score function is the identity function - A tale of characterizations of the normal distribution","authors":"Christophe Ley","doi":"10.1016/j.ecosta.2020.10.001","DOIUrl":"https://doi.org/10.1016/j.ecosta.2020.10.001","url":null,"abstract":"<div><p><span>The normal distribution is well-known for several results that it is the only to fulfil. Much less well-known is the fact that many of these characterizations follow from the fact that the derivative of the log-density of the normal distribution is the (negative) identity function. This </span><em>a priori</em> very simple yet surprising observation allows a deeper understanding of existing characterizations and paves the way for an immediate extension of various seemingly normal-based characterizations to a general density by replacing the (negative) identity function in these results with the derivative of that log-density.</p></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"26 ","pages":"Pages 153-160"},"PeriodicalIF":1.9,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.ecosta.2020.10.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50193295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Nearest neighbor matching: M-out-of-N bootstrapping without bias correction vs. the naive bootstrap","authors":"Christopher Walsh, C. Jentsch","doi":"10.1016/j.ecosta.2023.04.005","DOIUrl":"https://doi.org/10.1016/j.ecosta.2023.04.005","url":null,"abstract":"","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"194 1 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78326349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}